Print Email Facebook Twitter Evolution of robust high speed optical-flow-based landing for autonomous MAVs Title Evolution of robust high speed optical-flow-based landing for autonomous MAVs Author Scheper, K.Y.W. (TU Delft Control & Simulation) de Croon, G.C.H.E. (TU Delft Control & Simulation) Date 2020-02 Abstract Automatic optimization of robotic behavior has been the long-standing goal of Evolutionary Robotics. Allowing the problem at hand to be solved by automation often leads to novel approaches and new insights. A common problem encountered with this approach is that when this optimization occurs in a simulated environment, the optimized policies are subject to the reality gap when implemented in the real world. This often results in sub-optimal behavior, if it works at all. This paper investigates the automatic optimization of neurocontrollers to perform quick but safe landing maneuvers for a quadrotor micro air vehicle using the divergence of the optical flow field of a downward looking camera. The optimized policies showed that a piece-wise linear control scheme is more effective than the simple linear scheme commonly used, something not yet considered by human designers. Additionally, we show the utility in using abstraction on the input and output of the controller as a tool to improve the robustness of the optimized policies to the reality gap by testing our policies optimized in simulation on real world vehicles. We tested the neurocontrollers using two different methods to generate and process the visual input, one using a conventional CMOS camera and one a dynamic vision sensor, both of which perform significantly differently than the simulated sensor. The use of the abstracted input resulted in near seamless transfer to the real world with the controllers showing high robustness to a clear reality gap. Subject Evolutionary roboticsBio-inspired landingReality gapHigh speed flight To reference this document use: http://resolver.tudelft.nl/uuid:9a283c0c-e100-4931-90e8-b39ed1bad552 DOI https://doi.org/10.1016/j.robot.2019.103380 Embargo date 2021-12-13 ISSN 0921-8890 Source Robotics and Autonomous Systems, 124 Part of collection Institutional Repository Document type journal article Rights © 2020 K.Y.W. Scheper, G.C.H.E. de Croon Files PDF 1912.07735.pdf 4.77 MB Close viewer /islandora/object/uuid:9a283c0c-e100-4931-90e8-b39ed1bad552/datastream/OBJ/view